R: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

# Ensemble Learning Framework for Diagnostic and Prognostic Modeling

## Documentation for package ‘E2E’ version 0.1.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

apply_dia | Apply a Trained Model to New Data
apply_pro | Apply Prognostic Model to New Data
bagging_dia | Train a Bagging Diagnostic Model
bagging_pro | Train Bagging Ensemble for Prognosis
calculate_metrics_at_threshold_dia | Calculate Classification Metrics at a Specific Threshold
dt_dia | Train a Decision Tree Model for Classification
en_dia | Train an Elastic Net (L1 and L2 Regularized Logistic Regression) Model for Classification
en_pro | Train Elastic Net Cox Model
evaluate_model_dia | Evaluate Diagnostic Model Performance
evaluate_model_pro | Evaluate Prognostic Model Performance
evaluate_predictions_dia | Evaluate Predictions from a Data Frame
evaluate_predictions_pro | Evaluate External Predictions
figure_dia | Plot Diagnostic Model Evaluation Figures
figure_pro | Plot Prognostic Model Evaluation Figures
figure_shap | Generate and Plot SHAP Explanation Figures
find_optimal_threshold_dia | Find Optimal Probability Threshold
gbm_dia | Train a Gradient Boosting Machine (GBM) Model for Classification
gbm_pro | Train Gradient Boosting Machine (GBM) for Survival
get_registered_models_dia | Get Registered Diagnostic Models
get_registered_models_pro | Get Registered Prognostic Models
imbalance_dia | Train an EasyEnsemble Model for Imbalanced Classification
initialize_modeling_system_dia | Initialize Diagnostic Modeling System
initialize_modeling_system_pro | Initialize Prognosis Modeling System
int_dia | Comprehensive Diagnostic Modeling Pipeline
int_imbalance | Imbalanced Data Diagnostic Modeling Pipeline
int_pro | Comprehensive Prognostic Modeling Pipeline
lasso_dia | Train a Lasso (L1 Regularized Logistic Regression) Model for Classification
lasso_pro | Train Lasso Cox Proportional Hazards Model
lda_dia | Train a Linear Discriminant Analysis (LDA) Model for Classification
load_and_prepare_data_dia | Load and Prepare Data for Diagnostic Models
min_max_normalize | Min-Max Normalization
mlp_dia | Train a Multi-Layer Perceptron (Neural Network) Model for Classification
models_dia | Run Multiple Diagnostic Models
models_pro | Run Multiple Prognostic Models
nb_dia | Train a Naive Bayes Model for Classification
plot_integrated_results | Visualize Integrated Modeling Results
pls_pro | Train Partial Least Squares Cox (PLS-Cox)
predict_pro | Generic Prediction Interface for Prognostic Models
print_model_summary_dia | Print Diagnostic Model Summary
print_model_summary_pro | Print Prognostic Model Summary
qda_dia | Train a Quadratic Discriminant Analysis (QDA) Model for Classification
register_model_dia | Register a Diagnostic Model Function
register_model_pro | Register a Prognostic Model
rf_dia | Train a Random Forest Model for Classification
ridge_dia | Train a Ridge (L2 Regularized Logistic Regression) Model for Classification
ridge_pro | Train Ridge Cox Model
rsf_pro | Train Random Survival Forest (RSF)
stacking_dia | Train a Stacking Diagnostic Model
stacking_pro | Train Stacking Ensemble for Prognosis
stepcox_pro | Train Stepwise Cox Model (AIC-based)
Surv | re-export Surv from survival
svm_dia | Train a Support Vector Machine (Linear Kernel) Model for Classification
test_dia | Test Data for Diagnostic Models
test_pro | Test Data for Prognostic (Survival) Models
train_dia | Training Data for Diagnostic Models
train_pro | Training Data for Prognostic (Survival) Models
voting_dia | Train a Voting Ensemble Diagnostic Model
xb_dia | Train an XGBoost Tree Model for Classification
xgb_pro | Train XGBoost Cox Model